Nowadays, medical imaging reporting is one of the fundamental elements for diagnosis, prognosis and follow-up of patients. The role of medical imaging reporting is central to the diagnosis, surgical planning and treatment of patients in oncology, neurology and cardiology, among others. The knowledge of the underlying biological processes is opening a new perspective in managing patients, allowing the application of a more personalized, preventive and predictive medicine depending on the particular circumstance of each patient.
Currently, medical imaging protocols enhance anatomic features that are increasingly specific to tissues. Furthermore, certain protocols allow quantifying image biomarkers related to functional characteristics of tissues. The information obtained individually by each of these types of images may be combined in such a way that it permits describing derived information that is closer to the biological processes of interest for studying the disease.
Clinical areas of medical imaging need tools that provide doctors with tissue segmentations related to the biological processes underlying the diagnosis and/or prognosis of the disease. This capacity would permit reporting the expected progression of the patient and therefore choosing the particular treatment that provides best expected utility to the condition of the patient.
Quantitative medicine is a global trend that seeks to provide accurate information for medical decisions. Current approaches involve the extraction of biomarkers. However, the current biomarkers are based only on a medical image, which restricts the information provided by these biomarkers about the biological processes occurring in the patient's tissues. The definition of biomarkers from multiple complementary medical images may provide more and better information on the biological processes of tissues. Multi-parametric biomarkers are those biomarkers that are extracted from various medical images. There are several technical complications in obtaining multi-parametric biomarkers.
For example, it has been found for certain medical problems that the observation of features close to the biological processes and to the prognosis of the disease based on the usual procedure followed by medical imaging professionals is not possible. This is because the solution results from combining several medical images, and it cannot be observed through a single sequence. Such a difficulty does not permit designing automatic segmentation systems based on cases segmented by experts, since generating the set of cases has the following limitations:
1) it requires the professional to dedicate a long time (especially in 3D volumes), which limits the obtaining of labeled sets of cases,
2) it is not optimal for some difficult medical problems wherein the provision of labels for all tissues is not possible,
3) it is tedious for the professional, which leads to the rejection or laxity of the task,
4) it is poorly reproducible due to irregular and/or diffuse boundaries, and
5) it achieves results that are similar to those already achieved by the experts, so it does not add any value that permits to improve the radiological reporting.
Therefore, it is desirable to have tools that allow automatic and unsupervised obtaining of multi-parametric images that facilitate the diagnosis and treatment of the patients.
Some systems are already known in the art which allow obtaining images of this type. For example, Schad L et al. (MR tissue characterization of intracranial tumours by means of texture analysis. Magn. Reson. Imaging; 11 889-96; 1993) proposed for first time unsupervised models by clustering. The breakthrough in the development of ML (machine learning) techniques has resulted in more powerful classification algorithms that have rapidly been applied to medical imaging. Cai H et al. (Probabilistic segmentation of brain tumours based on multi-modality MRI. 4th IEEE Int. Symp. on Biomedical Imaging 600-3; 2007), among others, applied support vector machines (SVM) on sets of multi-parametric MRI images to obtain segmentation maps of healthy tissues and sub-compartments within the tumor area. Jensen T and Schmainda K. (Computer-aided detection of brain tumour invasion using multi-parametric MRI, J. Magn. Reson. Imaging, 30 481-9; 2009) explored different approaches based on neural networks also with a multi-parametric combination of anatomical and functional MRI images.
However, all these (and other) currently employed approaches assume that data is independent and identically distributed (i.i.d.). This strong assumption implies considering independence between voxels of the image, which leads to simple models but generally resulting in spatially non-consistent segmentation images, since they do not use the structural information provided by the images.
US20130094743 A1 relates to a method for evaluating tumour lesions by comparing images acquired at different times. The method may be used in different types of images and modalities and performs the registration, radial segmentation and quantification of areas or volumes along with visual presentation of results.
WO2008014340 A3 discloses a method of obtaining diffusion MRI images to create non-symmetric margins for radiotherapy.
However, these and other similar methods known in the prior art, for example, fail to perform a multi-parametric analysis or a segmentation that allows studying parts of the tissues related to biological characteristics, diagnosis, response to treatment and prognosis. They do not permit either obtaining a single multi-parametric nosological image from a stack of multiple medical images.
Therefore, there is a need in the art for a method and system of generating multi-parametric nosological images, from a stack of medical images, allowing easy identification of tissue subtypes related to biology, diagnosis, prognosis and/or response to treatment.